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The Impact of Interpretation Problems on Tutorial Dialogue
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In: DTIC (2010)
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| 2 |
Breaking the Resource Bottleneck for Multilingual Parsing
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In: DTIC AND NTIS (2005)
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| 3 |
Air Force Dual-Use Science and Technology Two-Way Voice-To-Voice Translator
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In: DTIC AND NTIS (2004)
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| 4 |
Consolidating the Results of the CIRCSIM-Tutor Project and Further Consolidation of the Results of the CIRCSIM-Tutor Project
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In: DTIC AND NTIS (2003)
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| 5 |
Technical Opportunities to Help with the Year 2000 Problem
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In: DTIC AND NTIS (1997)
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| 6 |
Diphone-Based Speech Recognition Using Neural Networks.
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In: DTIC AND NTIS (1996)
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| 7 |
A Robust Loose Coupling for Speech Recognition and Natural Understanding.
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In: DTIC AND NTIS (1995)
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| 8 |
The Use of Fuzzy Set Classification for Pattern Recognition of the Polygraph. Volume 2.
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In: DTIC AND NTIS (1995)
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| 9 |
Spoken Dialogue Understanding and Local Context.
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In: DTIC AND NTIS (1994)
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| 10 |
Segment-Based Acoustic Models for Continuous Speech Recognition.
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In: DTIC AND NTIS (1994)
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| 11 |
High-Performance Speech Recognition Using Consistency Modeling.
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In: DTIC AND NTIS (1994)
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Abstract:
The goal of SRI's consistency modeling project is to improve the raw acoustic modeling component of SRI's DECIPHER speech recognition system and develop consistency modeling technology. Consistency modeling aims to reduce the number of improper independence assumptions used in traditional speech recognition algorithms so that the resulting speech recognition hypotheses are more self-consistent and, therefore, more accurate. At the initial stages of this effort, SRI focused on developing the appropriate base technologies for consistency modeling. We first developed the Progressive Search technology that allowed us to perform large-vocabulary continuous speech recognition (LVCSR) experiments. Since its conception and development at SRI, this technique has been adopted by most laboratories, including other ARPA contracting sites, doing research on LVSR. Another goal of the consistency modeling project is to attack difficult modeling problems, when there is a mismatch between the training and testing phases. Such mismatches may include outlier speakers, different microphones and additive noise. We were able to either develop new, or transfer and evaluate existing, technologies that adapted our baseline genonic HMM recognizer to such difficult conditions. (AN)
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Keyword:
*PATTERN RECOGNITION; *SPEECH RECOGNITION; ACOUSTIC FILTERS; ACOUSTIC SIGNALS; Acoustics; ADAPTIVE SYSTEMS; ALGORITHMS; BACKGROUND NOISE; CONSISTENCY; Cybernetics; ERROR CORRECTION CODES; EXPERIMENTAL DATA; KNOWLEDGE BASED SYSTEMS; Linguistics; LPN-SRI-3773; MARKOV PROCESSES; MICROPHONES; MODELS; NATURAL LANGUAGE; PERFORMANCE(ENGINEERING); REAL TIME; VOCABULARY; Voice Communications; WORD RECOGNITION
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URL: http://www.dtic.mil/docs/citations/ADA290655
http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA290655
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| 12 |
A Speech Controlled Information-Retrieval System
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In: DTIC AND NTIS (1983)
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| 13 |
Interactive Natural Language Problem Solving: A Pragmatic Approach
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In: DTIC AND NTIS (1983)
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| 14 |
Adaptive Understanding: Correcting Erroneous Inferences.
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In: DTIC AND NTIS (1980)
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| 15 |
Structure-Preserved Error-Correcting Tree Automata for Syntactic Pattern Recognition
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In: DTIC AND NTIS (1976)
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| 16 |
Abstract Data Types and Software Validation
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In: DTIC AND NTIS (1976)
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| 17 |
Error Correcting Codes for the English Alphabet and Generalizations
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In: DTIC AND NTIS (1972)
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| 18 |
PROBABILITIES IN CONTEXT-FREE PROGRAMMED GRAMMARS
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In: DTIC AND NTIS (1970)
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| 19 |
Practicing Correction Codes to Improve English Writing Skills
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In: http://www.arcjournals.org/pdfs/ijsell/v2-i8/2.pdf
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